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Online detection and quantification of epidemics.
Camille Pelat1, Pierre-Yves Boëlle, Benjamin J Cowling
1Université Pierre et Marie Curie-Paris 6, UMR-S 707, Paris, 75012 France. pelat@u707.jussieu.fr
BMC Medical Informatics and Decision Making
|October 17, 2007
Summary
An online tool simplifies analyzing health time series data for disease surveillance. It helps detect outbreaks and quantify epidemic impact, making complex analysis accessible to public health practitioners.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Health care generates increasing time series data for disease surveillance.
- Periodic regression models are used for outbreak detection but require statistical expertise.
- A lack of dedicated tools hinders the analysis of epidemiologic time series.
Purpose of the Study:
- To develop an accessible online tool for analyzing epidemiologic time series data.
- To enable easy detection and quantification of disease outbreaks and their burden.
- To guide users through the analysis of periodic baseline levels and epidemic events.
Main Methods:
- Development of an online computer application for time series analysis.
- Implementation of periodic regression models to estimate baseline levels and forecast limits.
- Definition of epidemic burden as excess signal above the estimated baseline.
Main Results:
- An online application is available for analyzing epidemiologic time series.
- The tool estimates periodic baseline levels and epidemic thresholds for outbreak detection.
- Demonstrated application in retrospective analysis of pneumonia and influenza mortality and prospective surveillance of diarrheal disease.
Conclusions:
- The online application facilitates the detection of unusual events in health time series.
- It enables straightforward quantification of excess mortality or morbidity.
- This tool is valuable for public health practitioners in field and surveillance settings.
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